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  <title>Description of histc_sift_nD</title>
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<h1>histc_sift_nD
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<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Creates a series of locally position dependent histograms.</strong></div>

<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function hs = histc_sift_nD( I, edges, pargmask, weightmask, multch ) </strong></div>

<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Creates a series of locally position dependent histograms.

 Creates a series of locally position dependent histograms of the values in the mutiple
 channel multidimensional array I (this is a generalized version of histc_sift that
 allows for multiple channels).

 I is an M1xM2x...xMkxnd array, it consists of nd channels each of dimension
 (M1xM2x...xMk).  histc_sift_nD works by dividing a (M1xM2x...xMk) array into seperate
 regions and creating a 1D histogram for each using histc_1D.  histc_sift_nD does the same
 thing except each region now has multiple channels, and an nd-dimensional histogram is
 created for each using histc_nD.

 INPUTS
   I           - M1xM2x...xMkxnd array, (nd channels each of dim M1xM2x...xMk)
   edges       - parameter to histc_nD, [either scalar, vector, or cell vector]
   pargmask    - cell of parameters to mask_gaussians
   weightmask  - [optional] M1xM2x...xMk numeric array of weights
   multch      - [optional] if 0 this becomes same as histc_sift.m (nd==1)

 OUTPUTS
   hs          - histograms (array of size nmasks x nbins)

 EXAMPLE
   G = filter_gauss_nD([100 100],[],[],0); 
   hs = histc_sift_nD( cat(3,G,G), 5, {2,.6,.1,0} ); 
   hs = histc_sift_nD( cat(3,G,randn(size(G))),5,{2,.6,.1,0}); 
   figure(1); montage2(hs,1);  figure(2); montage2(hs,1);

 DATESTAMP
   29-Sep-2005  2:00pm

 See also <a href="histc_1D.html" class="code" title="function h = histc_1D( I, edges, weightmask )">HISTC_1D</a>, <a href="histc_sift.html" class="code" title="function hs = histc_sift( I, edges, pargmask, weightmask )">HISTC_SIFT</a>, <a href="mask_gaussians.html" class="code" title="function [masks,keeplocs] = mask_gaussians( siz, M, windowwidth, offset, show )">MASK_GAUSSIANS</a>, <a href="histc_nD.html" class="code" title="function h = histc_nD( I, edges, weightmask )">HISTC_ND</a></pre></div>

<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="gauss_smooth.html" class="code" title="function [L,filters] = gauss_smooth( I, sigmas, shape, radius )">gauss_smooth</a>	Applies Gaussian smoothing to a (multidimensional) image.</li><li><a href="histc_1D.html" class="code" title="function h = histc_1D( I, edges, weightmask )">histc_1D</a>	Generalized, version of histc (histogram count), allows weighted values.</li><li><a href="histc_nD.html" class="code" title="function h = histc_nD( I, edges, weightmask )">histc_nD</a>	Generalized, multidimensional version of normalized histc (histogram count).</li><li><a href="mask_gaussians.html" class="code" title="function [masks,keeplocs] = mask_gaussians( siz, M, windowwidth, offset, show )">mask_gaussians</a>	Divides a volume into softly overlapping gaussian windows.</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="histc_sift.html" class="code" title="function hs = histc_sift( I, edges, pargmask, weightmask )">histc_sift</a>	Creates a series of locally position dependent histograms of the values in I.</li></ul>
<!-- crossreference -->



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